Integrate AI-powered document pipeline into your applications
See the MarieAI docs.
You don't need this source code unless you want to modify the package. If you just want to use the package, just run:
pip install --upgrade marieaiInstall from source with:
pip install -e .Build docker container:
DOCKER_BUILDKIT=1 docker build . --build-arg PIP_TAG="standard" -f ./Dockerfiles/gpu.Dockerfile -t marieai/marie:3.0-cuda This library additionally provides an marie command-line utility which makes it easy to interact with the API
from your terminal. Run marie -h for usage.
Examples of how to use this library to accomplish various tasks can be found in the MarieAI documentation. It contains code examples for:
- Document cleanup
- Optical character recognition (OCR)
- Document Classification
- Document Splitter
- Named Entity Recognition
- Form detection
- And more
docker run --rm -it marieai/marie:3.0.19-cudadocker run --rm -it --entrypoint /bin/bash marieai/marie:3.0.30-cuda
marie server --start --uses sample.yml TODO :MOVE TO DOCS
docker compose -f docker-compose.s3.yml --project-directory . up --build --remove-orphansCrossFTP
vi ~/.aws/credentials
[marie] # this should be in the file
aws_access_key_id=your_access_key_id
aws_secret_access_key=your_secret_access_keydocker pull zenko/cloudserverdocker run --rm -it --name marie-s3-server -p 8000:8000 \
-e SCALITY_ACCESS_KEY_ID=MARIEACCESSKEY \
-e SCALITY_SECRET_ACCESS_KEY=MARIESECRETACCESSKEY \
-e S3DATA=multiple \
-e S3BACKEND=mem zenko/cloudserverSCALITY_ACCESS_KEY_ID : Your AWS ACCESS KEY
SCALITY_SECRET_ACCESS_KEY: Your AWS SECRET ACCESS KEY
S3BACKEND: Currently using memory storage
aws s3 mb s3:https://mybucket --profile marie --endpoint-url https://localhost:8000 --region us-west-2aws s3 ls --profile marie --endpoint-url https://localhost:8000aws s3 cp some_file.txt s3:https://mybucket --profile marie --endpoint-url https://localhost:8000aws s3 --profile marie --endpoint-url=https://127.0.0.1:8000 ls --recursive s3:https://Remove files from the bucket
aws s3 rm s3:https://marie --recursive --profile marie --endpoint-url https://localhost:8000Configuration for the S3 server will be stored in the following files: https://towardsdatascience.com/10-lessons-i-learned-training-generative-adversarial-networks-gans-for-a-year-c9071159628